Biblio
We consider the problem of verifying the security of finitely many sessions of a protocol that tosses coins in addition to standard cryptographic primitives against a Dolev-Yao adversary. Two properties are investigated here - secrecy, which asks if no adversary interacting with a protocol P can determine a secret sec with probability textgreater 1 - p; and indistinguishability, which asks if the probability observing any sequence 0$øverline$ in P1 is the same as that of observing 0$øverline$ in P2, under the same adversary. Both secrecy and indistinguishability are known to be coNP-complete for non-randomized protocols. In contrast, we show that, for randomized protocols, secrecy and indistinguishability are both decidable in coNEXPTIME. We also prove a matching lower bound for the secrecy problem by reducing the non-satisfiability problem of monadic first order logic without equality.
In this paper, we present a decentralized nonlinear robust controller to enhance the transient stability margin of synchronous generators. Although, the trend in power system control is shifting towards centralized or distributed controller approaches, the remote data dependency of these schemes fuels cyber-physical security issues. Since the excessive delay or losing remote data affect severely the operation of those controllers, the designed controller emerges as an alternative for stabilization of Smart Grids in case of unavailability of remote data and in the presence of plant parametric uncertainties. The proposed controller actuates distributed storage systems such as flywheels in order to reduce stabilization time and it implements a novel input time delay compensation technique. Lyapunov stability analysis proves that all the tracking error signals are globally uniformly ultimately bounded. Furthermore, the simulation results demonstrate that the proposed controller outperforms traditional local power systems controllers such as Power System Stabilizers.
In Wyner wiretap II model of communication, Alice and Bob are connected by a channel that can be eavesdropped by an adversary with unlimited computation who can select a fraction of communication to view, and the goal is to provide perfect information theoretic security. Information theoretic security is increasingly important because of the threat of quantum computers that can effectively break algorithms and protocols that are used in today's public key infrastructure. We consider interactive protocols for wiretap II channel with active adversary who can eavesdrop and add adversarial noise to the eavesdropped part of the codeword. These channels capture wireless setting where malicious eavesdroppers at reception distance of the transmitter can eavesdrop the communication and introduce jamming signal to the channel. We derive a new upperbound R ≤ 1 - ρ for the rate of interactive protocols over two-way wiretap II channel with active adversaries, and construct a perfectly secure protocol family with achievable rate 1 - 2ρ + ρ2. This is strictly higher than the rate of the best one round protocol which is 1 - 2ρ, hence showing that interaction improves rate. We also prove that even with interaction, reliable communication is possible only if ρ \textbackslashtextless; 1/2. An interesting aspect of this work is that our bounds will also hold in network setting when two nodes are connected by n paths, a ρ of which is corrupted by the adversary. We discuss our results, give their relations to the other works, and propose directions for future work.
We report on our research on proving the security of multi-party cryptographic protocols using the EASYCRYPT proof assistant. We work in the computational model using the sequence of games approach, and define honest-butcurious (semi-honest) security using a variation of the real/ideal paradigm in which, for each protocol party, an adversary chooses protocol inputs in an attempt to distinguish the party's real and ideal games. Our proofs are information-theoretic, instead of being based on complexity theory and computational assumptions. We employ oracles (e.g., random oracles for hashing) whose encapsulated states depend on dynamically-made, nonprogrammable random choices. By limiting an adversary's oracle use, one may obtain concrete upper bounds on the distances between a party's real and ideal games that are expressed in terms of game parameters. Furthermore, our proofs work for adaptive adversaries, ones that, when choosing the value of a protocol input, may condition this choice on their current protocol view and oracle knowledge. We provide an analysis in EASYCRYPT of a three party private count retrieval protocol. We emphasize the lessons learned from completing this proof.
Feature selection is an important step in data analysis to address the curse of dimensionality. Such dimensionality reduction techniques are particularly important when if a classification is required and the model scales in polynomial time with the size of the feature (e.g., some applications include genomics, life sciences, cyber-security, etc.). Feature selection is the process of finding the minimum subset of features that allows for the maximum predictive power. Many of the state-of-the-art information-theoretic feature selection approaches use a greedy forward search; however, there are concerns with the search in regards to the efficiency and optimality. A unified framework was recently presented for information-theoretic feature selection that tied together many of the works in over the past twenty years. The work showed that joint mutual information maximization (JMI) is generally the best options; however, the complexity of greedy search for JMI scales quadratically and it is infeasible on high dimensional datasets. In this contribution, we propose a fast approximation of JMI based on information theory. Our approach takes advantage of decomposing the calculations within JMI to speed up a typical greedy search. We benchmarked the proposed approach against JMI on several UCI datasets, and we demonstrate that the proposed approach returns feature sets that are highly consistent with JMI, while decreasing the run time required to perform feature selection.
Physical consequences to power systems of false data injection cyber-attacks are considered. Prior work has shown that the worst-case consequences of such an attack can be determined using a bi-level optimization problem, wherein an attack is chosen to maximize the physical power flow on a target line subsequent to re-dispatch. This problem can be solved as a mixed-integer linear program, but it is difficult to scale to large systems due to numerical challenges. Three new computationally efficient algorithms to solve this problem are presented. These algorithms provide lower and upper bounds on the system vulnerability measured as the maximum power flow subsequent to an attack. Using these techniques, vulnerability assessments are conducted for IEEE 118-bus system and Polish system with 2383 buses.
Address clustering tries to construct the one-to-many mapping from entities to addresses in the Bitcoin system. Simple heuristics based on the micro-structure of transactions have proved very effective in practice. In this paper we describe the primary reasons behind this effectiveness: address reuse, avoidable merging, super-clusters with high centrality,, the incremental growth of address clusters. We quantify their impact during Bitcoin's first seven years of existence.
In data analysis, it is always a tough task to strike the balance between the privacy and the applicability of the data. Due to the demand for individual privacy, the data are being more or less obscured before being released or outsourced to avoid possible privacy leakage. This process is so called de-identification. To discuss a de-identification policy, the most important two aspects should be the re-identification risk and the information loss. In this paper, we introduce a novel policy searching method to efficiently find out proper de-identification policies according to acceptable re-identification risk while retaining the information resided in the data. With the UCI Machine Learning Repository as our real world dataset, the re-identification risk can therefore be able to reflect the true risk of the de-identified data under the de-identification policies. Moreover, using the proposed algorithm, one can then efficiently acquire policies with higher information entropy.
Techniques for network security analysis have historically focused on the actions of the network hosts. Outside of forensic analysis, little has been done to detect or predict malicious or infected nodes strictly based on their association with other known malicious nodes. This methodology is highly prevalent in the graph analytics world, however, and is referred to as community detection. In this paper, we present a method for detecting malicious and infected nodes on both monitored networks and the external Internet. We leverage prior community detection and graphical modeling work by propagating threat probabilities across network nodes, given an initial set of known malicious nodes. We enhance prior work by employing constraints that remove the adverse effect of cyclic propagation that is a byproduct of current methods. We demonstrate the effectiveness of probabilistic threat propagation on the tasks of detecting botnets and malicious web destinations.
In this paper, we consider the security of exact-repair regenerating codes operating at the minimum-storage-regenerating (MSR) point. The security requirement (introduced in Shah et. al.) is that no information about the stored data file must be leaked in the presence of an eavesdropper who has access to the contents of ℓ1 nodes as well as all the repair traffic entering a second disjoint set of ℓ2 nodes. We derive an upper bound on the size of a data file that can be securely stored that holds whenever ℓ2 ≤ d - k + 1. This upper bound proves the optimality of the product-matrix-based construction of secure MSR regenerating codes by Shah et. al.
We consider the block Rayleigh fading multiple-input multiple-output (MIMO) wiretap channel with no prior channel state information (CSI) available at any of the terminals. The channel gains remain constant in a coherence time of T symbols, and then change to another independent realization. The transmitter, the legitimate receiver and the eavesdropper have nt, nr and ne antennas, respectively. We determine the exact secure degrees of freedom (s.d.o.f.) of this system when T ≥ 2 min(nt, nr). We show that, in this case, the s.d.o.f. is exactly (min(nt, nr) - ne)+(T - min(nt, nr))/T. The first term can be interpreted as the eavesdropper with ne antennas taking away ne antennas from both the transmitter and the legitimate receiver. The second term can be interpreted as a fraction of s.d.o.f. being lost due to the lack of CSI at the legitimate receiver. In particular, the fraction loss, min(nt, nr)/T, can be interpreted as the fraction of channel uses dedicated to training the legitimate receiver for it to learn its own CSI. We prove that this s.d.o.f. can be achieved by employing a constant norm channel input, which can be viewed as a generalization of discrete signalling to multiple dimensions.
In this paper, we consider the security of exact-repair regenerating codes operating at the minimum-storage-regenerating (MSR) point. The security requirement (introduced in Shah et. al.) is that no information about the stored data file must be leaked in the presence of an eavesdropper who has access to the contents of ℓ1 nodes as well as all the repair traffic entering a second disjoint set of ℓ2 nodes. We derive an upper bound on the size of a data file that can be securely stored that holds whenever ℓ2 ≤ d - k + 1. This upper bound proves the optimality of the product-matrix-based construction of secure MSR regenerating codes by Shah et. al.